CN108388121B - Fuzzy logic control method of mechanical movable sieve jig - Google Patents
Fuzzy logic control method of mechanical movable sieve jig Download PDFInfo
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract
A fuzzy logic control method of a mechanical movable sieve jig solves the problems that parameter setting difficulty is high, the control is not suitable for a nonlinear and hysteretic system, and the actual waste rock discharge effect is not ideal in the prior art. The method comprises the steps of setting a gangue layer power value according to the no-load power of equipment, and collecting the power of a main driving motor of the mechanical moving screen jigger in real time through a power transmitter; fuzzification processing is carried out on the power deviation, the deviation change rate and the gangue discharge curve correction rate of the movable sieve, and then a fuzzy rule table is manufactured by combining the requirement and the actual experience of the gangue discharge control process of the mechanical jigger; defuzzification is carried out by a weighted average method to obtain a correction rate of the waste discharge curve; and then, curvature adjustment is carried out on a waste rock discharge control curve formula by utilizing a waste rock discharge curve correction rate obtained by fuzzy control, and an output value is automatically adjusted. The method can perform fuzzy judgment according to the change of the raw coal quantity, realize the intelligent control of the speed change of the gangue discharge wheel, improve the separation effect of the raw coal and have strong adaptability.
Description
Technical Field
The invention belongs to the technical field of coal washing, relates to a coal and gangue separation method, and particularly relates to a fuzzy logic control method of a mechanical movable sieve jig, which can perform fuzzy judgment according to the change of the quantity of raw coal, realize intelligent control on the speed change of a gangue discharge wheel, improve the raw coal separation effect and has strong adaptability.
Background
After raw coal enters a preparation workshop of a coal preparation plant or a movable sieve body of a mechanical movable sieve jig in a raw coal separation system under a coal mine, the falling speed of waste rocks suspended in water is relatively high due to the fact that the specific gravity of the coal is relatively small and the specific gravity of the waste rocks is relatively large; through repeated vibration of the screen body, gangue is stored on the upper edge of the screen surface, and the coal bed is suspended in the upper half part of water. A group of baffles and a gangue discharge wheel are arranged at the outlet of the screen body of the movable screen, coal is discharged from the upper surface of the gangue discharge wheel, and gangue forms a gangue layer at the lower side of the baffles.
When the mechanical movable sieve jig works, if the rotating speed of the waste rock discharging wheel is lower, the waste rock layer is thicker and thicker, so that the waste rock enters a coal discharging channel, and the phenomenon of waste rock clamping in coal occurs. And if the rotation speed of the gangue discharge wheel is higher, the gangue layer becomes thinner, and lump coal is possibly discharged from the gangue channel, so that the phenomenon of carrying coal in gangue is generated. Therefore, the rotation speed of the gangue discharge wheel needs to be properly adjusted, the thickness of the gangue layer needs to be reasonably controlled, coal and gangue can be stably discharged according to respective channels, and a good coal washing effect is achieved.
The existing mode for controlling waste rock discharge comprises the following steps:
1. and (4) PID control. The program structure is simple, the reliability is high, and the steady-state precision is good; however, because of the existence of many non-linearities and time-varying properties of each process parameter and the difficulty in setting the parameters of the PID controller, it is difficult to find a suitable set of PID parameters for the wide-range regulation of the whole system, and the control effect is not good.
2. Fuzzy PID control. The method has the advantages of strong self-adaptive capacity, automatic setting of control parameters and the like; however, the main control purpose of the method is to strive for quickly stabilizing the thickness of a gangue bed layer, once the input quantity of raw coal changes, the frequency of a frequency converter can quickly rise and fall, large discharge and amplification occur, and then the interlayer structure of gangue and coal in a screen body changes, the adaptability is poor, and the actual gangue discharge effect is not ideal.
3. And (4) controlling logic linearity. The mathematical model is simple, the online parameter adjustability is high, and the adaptability is strong; but the steady-state precision is not high, the reasoning speed is slow, and the method is not suitable for the control of a nonlinear and hysteretic system.
Particularly, in a coal mine underground raw coal separation system, due to the influences of various factors such as different raw coal amount, uneven feeding, different coal-gangue proportion and the like caused by the fact that underground coal mining passes through a fault, an underground mechanical movable sieve jig coal washing system has the characteristics of high nonlinearity, time-varying uncertainty, lag and the like, so that the existing control mode cannot meet the actual application requirements, and therefore the control method of the existing mechanical movable sieve jig is required to be improved.
Disclosure of Invention
The invention aims at the problems and provides a fuzzy logic control method of a mechanical movable sieve jig, which can perform fuzzy judgment according to the change of the quantity of raw coal, realize the intelligent control of the speed change of a gangue discharge wheel, improve the sorting effect of the raw coal and have strong adaptability.
Aiming at the problems in the prior art, the invention provides a control method combining fuzzy control and logic curve control, wherein the fuzzy logic control is not as good as fuzzy PID control in the precision of stable bed thickness, but gives consideration to frequency lifting change, and comprehensively considers bed thickness and waste rock discharge frequency to achieve an ideal waste rock discharge effect.
The technical scheme adopted by the invention is as follows: the fuzzy logic control method of the mechanical movable sieve jig comprises the following steps:
step one, setting a gangue layer power value according to the no-load power of the mechanical movable sieve jig;
step two, acquiring the power P of the main driving motor of the mechanical moving screen jig in real time through a power transmitter, and transmitting the power value of the power P of the main driving motor of the mechanical moving screen jig to a PLC in an analog quantity form;
step three, taking the PLC as a fuzzy controller, and fuzzifying the power deviation e, the deviation change rate delta e and the gangue discharge curve correction rate u of the main driving motor of the mechanical moving screen jig;
fourthly, according to the waste rock discharge control process requirement of the mechanical movable sieve jig, combining practical experience to manufacture a fuzzy rule table, defuzzifying by a weighted average method, and then obtaining a waste rock discharge curve correction rate u;
fifthly, determining a gangue discharge control curve formula Ft according to the rated power of a main driving motor of the mechanical moving screen jigger and the upper and lower limit values set by no-load power;
and step six, curvature adjustment is carried out on a waste rock discharge control curve formula by using a waste rock discharge curve correction rate u obtained through fuzzy control, so that the waste rock discharge control curve can carry out fuzzy judgment according to different working conditions, and the frequency value of the waste rock discharge frequency converter is automatically adjusted.
And step three, dividing three signals of the power deviation e, the deviation change rate delta e and the gangue discharge curve correction rate u of the main driving motor of the mechanical moving screen jig into 7 fuzzy states, namely: NB, NM, NS, ZO, PS, PM, PB; and the membership function is a triangular function, and then fuzzy reasoning is carried out according to a fuzzy algorithm.
Obtaining NxN rules by a fuzzy rule table, wherein each rule corresponds to different power deviation e, deviation change rate delta e and language values of the controlled variable; and then obtaining the fuzzy subsets corresponding to the language values according to the membership function.
Step five, combining the requirements of the mechanical movable sieve jig on the waste rock discharge wheel motor, and when the thickness of the waste rock layer is lower than the height of a waste rock discharge port, the rotating speed of the waste rock discharge wheel motor is 0; when the thickness of the gangue layer exceeds the height of the gangue outlet, starting a gangue discharge wheel motor; the rotating speed of the motor of the waste rock discharging wheel is continuously increased along with the increase of the thickness of the waste rock layer, and the rotating speed of the waste rock discharging wheel is fastest when the thickness of the waste rock layer reaches the upper edge of the baffle; the formula of the waste rock discharge control curve is as follows:
in the formula: pt is the real-time power of a main driving motor of the mechanical moving screen jig;
ph is an upper limit value of real-time power of a main driving motor of the mechanical moving screen jigger;
p1-lower limit value of real-time power of a main driving motor of the mechanical moving screen jigger;
ft is the real-time frequency of the normal operation of the gangue discharge wheel motor.
And step six, detecting the field data in real time by the system detection device, comparing the field data with a given value, performing A/D conversion and quantization to obtain a basic domain variable, looking up a table and outputting a fuzzy language value U, and performing fuzzy judgment to obtain a clear quantity: and (4) correcting the gangue discharge curve.
The invention has the beneficial effects that: the fuzzy logic control method of the mechanical movable sieve jig comprises the steps of setting a gangue layer power value according to the no-load power of equipment, collecting the power of a main driving motor of the mechanical movable sieve jig in real time through a power transmitter, and transmitting the power value to a PLC (programmable logic controller); fuzzification processing is carried out on the power deviation, the deviation change rate and the gangue discharge curve correction rate of the movable sieve, and then a fuzzy rule table is manufactured by combining the requirement and the actual experience of the gangue discharge control process of the mechanical jigger; defuzzification is carried out by a weighted average method to obtain a correction rate of the waste discharge curve; and then, curvature adjustment is carried out on a waste rock discharge control curve formula by utilizing a waste rock discharge curve correction rate obtained by fuzzy control, so that the waste rock discharge control curve can carry out fuzzy judgment according to different working conditions, and the output value is automatically adjusted. Because the invention adopts the control method of combining fuzzy control and logic curve control, fuzzy judgment can be carried out according to the change of the raw coal incoming quantity, the correction rate of the gangue discharge control output curve is automatically adjusted, and the intelligent control of the change of the gangue discharge wheel speed is realized. When the raw coal input fluctuation is large, the change of the thickness of the gangue layer in the mechanical moving sieve jig controlled by the fuzzy logic is smaller than that of the traditional logic control, and the gangue layer can be quickly recovered to be stable, so that the raw coal sorting effect of the whole mechanical moving sieve jig is improved, and the adaptability of the equipment to different coal quality conditions and particle size ranges is enhanced.
Drawings
FIG. 1 is a graph of membership functions for variables in the present invention.
FIG. 2 is a graph of the designed operating characteristics of the gangue dumping wheel motor in the invention.
Fig. 3 is a functional block diagram of a control routine of the present invention.
FIG. 4 is a flow chart of a fuzzy logic control method of the present invention.
FIG. 5 is a graph of the amount of gangue in the moving screen and the frequency of gangue discharge when the amount of feed fluctuates in a small range.
FIG. 6 is a graph of the amount of gangue in the moving screen and the frequency of gangue discharge when no material is fed in 40 seconds.
FIG. 7 is a graph of the amount of gangue in the moving screen and the frequency of gangue discharge when the amount of feed is continuously 50 units in 40 seconds.
The curves in fig. 5 to 7 illustrate: p1 is a curve of the amount of gangue in the moving screen under the control of fuzzy logic, and F1 is a curve of the gangue discharge frequency under the control of fuzzy logic; p2 is a curve of the amount of gangue in the moving screen under logic linear control, and F2 is a curve of the gangue discharge frequency under logic linear control.
Detailed Description
The specific steps of the present invention are explained in detail. The fuzzy logic control method of the mechanical movable sieve jig comprises the following steps:
step one, setting a gangue layer power value according to the no-load power of the mechanical movable sieve jig; the power value of the gangue layer needs to be adjusted and set according to the coal quality on site, and is generally about 10KW plus no-load power.
And step two, acquiring the power P of the main driving motor of the mechanical moving screen jig in real time through a power transmitter, and transmitting the power value of the power P of the main driving motor of the mechanical moving screen jig to the PLC in the form of analog quantity (0-10V) for calculation.
Step three, taking the PLC as a fuzzy controller, and fuzzifying the power deviation e, the deviation change rate delta e and the gangue discharge curve correction rate u of the main driving motor of the mechanical moving screen jig; and dividing three signals of the power deviation e, the deviation change rate delta e and the gangue discharge curve correction rate u of the main driving motor of the mechanical moving screen jigger into 7 fuzzy states, namely: NB, NM, NS, ZO, PS, PM, PB. Linguistic variables, fundamental discourse domain, fuzzy subset, and fuzzy discourse domain of the three signal variables are shown in table 1.
Table 1 variable data table
Then, the three variables are discretized according to the following formula:
in the formula: x belongs to [ a, b ], n is the dispersion of variables e, delta e and u.
Selecting the membership function of each variable as a triangle, and making a membership function graph (shown in FIG. 1) of each variable according to Table 1.
And step four, manufacturing a fuzzy rule table (shown in table 2) according to the gangue discharge control process requirements of the mechanical jigger and by combining actual experience. The control rules are all "if E is Ei and Δ E is Δ Ei the U is Ui, i is 1, 2, … 7"; for example: if E equals NB and Δ E equals PS, then U equals NM.
TABLE 2 fuzzy rule Table
In the table: NB-is large; NM-minus; NS-minus is small; ZO-zero; PS-plus-minus is small; PM-median; PB-Zhengda
Then, fuzzy inference is performed according to a fuzzy algorithm. Rule 49(7 × 7 ═ 49) is obtained from the fuzzy rule table in table 2; each rule corresponds to a different power deviation, deviation change rate and linguistic value of the controlled variable. And then, a fuzzy set assignment table (shown in table 3) of the power deviation E, the deviation change rate delta E and the control quantity U is obtained according to the membership function.
Table 3 fuzzy sets A, B, C assignment table
Fuzzy subsets A1-A7, B1-B7 and C1-C7 corresponding to all language values.
Fuzzy subsets a1, B1, C1 ═ 10.500000;
fuzzy subsets a2, B2, C2 ═ 010.50000;
fuzzy subsets a3, B3, C3 ═ 00.510.5000;
fuzzy subsets a4, B4, C4 ═ 000.510.500;
fuzzy subsets a5, B5, C5 ═ 0000.510.50;
fuzzy subsets a6, B6, C6 ═ 00000.510;
fuzzy subsets a7, B7, C7 ═ 000000.51.
A fuzzy relation submatrix RAi of the fuzzy set A is AixCij;
the fuzzy relation submatrix RBj of the fuzzy set B is Bj multiplied by Cij (i is 1, 2, … 23, j is 1, 2, … 41), 23 and 41 relation submatrices can be obtained according to the method, and all the submatrices are collected to obtain the sumTo the fuzzy relation matrix RA ═ u RAi and RB ═ u RBj. The analog output can be found as:
the analog quantity obtained above is output as a fuzzy vector of 1 × 7, and the row element (u (zij)) of each row corresponds to the corresponding discrete variable zj, so that the fuzzy is solved by a weighted average method formula to obtain a correction rate u of the gangue discharge curve:
step five, combining the requirements of the mechanical movable sieve jig on the waste rock discharge wheel motor, and when the thickness of the waste rock layer is lower than the height of a waste rock discharge port, the rotating speed of the waste rock discharge wheel motor is 0; when the thickness of the gangue layer exceeds the height of the gangue outlet, starting a gangue discharge wheel motor; and the rotating speed of the motor of the waste rock discharging wheel is continuously increased along with the increase of the thickness of the waste rock layer, and the rotating speed of the waste rock discharging wheel is fastest when the thickness of the waste rock layer reaches the upper edge of the baffle. Determining a gangue discharge control curve formula according to the rated power of a main driving motor of the mechanical moving screen jigger and the upper and lower limit values set by the power:
in the formula: pt is the real-time power of a main driving motor of the mechanical moving screen jig;
ph-an upper limit value (which can be set on site) of real-time power of a main drive motor of the mechanical moving sieve jig;
p1-lower limit value (field setting) of real-time power of main drive motor of mechanical moving screen jigger;
ft is the real-time frequency of the normal operation of the gangue discharge wheel motor.
The designed operating characteristic curve of the gangue dumping wheel motor is shown in figure 2.
And step six, carrying out curvature adjustment on a waste rock discharge control curve formula by using a waste rock discharge curve correction rate u obtained by fuzzy control, so that the waste rock discharge control curve can be subjected to fuzzy judgment according to different working conditions. The system detection device detects the field data in real time, compares the field data with a given value, and outputs a fuzzy language value U after being converted and quantized into a basic domain variable by A/D (analog/digital) conversion, and then carries out fuzzy judgment to obtain a clear quantity: and (4) correcting the gangue discharge curve.
The obtained change rule of the gangue discharge frequency is as follows:
in the formula: a. b is the maximum value and the minimum value of the basic discourse domain of the variables e, delta e and u, and n is the dispersion of the variables e, delta e and u;
according to the fuzzy logic formula:
converting the obtained output quantity into 4-20 mA direct current signals through D/A (digital/analog) to a frequency converter; thereby automatically adjusting the frequency value of the gangue discharge frequency converter.
The specific embodiment is as follows:
the lower limit value of the main driving motor of the mechanical moving sieve jigger is set to be 35KW, and the upper limit value of the main driving motor of the mechanical moving sieve jigger is set to be 75 KW. The hardware system adopts an industrial personal computer and a Panasonic FPX-C60R programmable controller as fuzzy controllers, and the tasks mainly completed by the computer are data storage, real-time monitoring and power signal simulation. The PLC is used as an actuator, receives the power signal transmitted by the computer, compares the power signal with the power bed layer signal collected by the PLC, and sends the error and the error change to the interior of the PLC for fuzzy processing. Then obtaining the control frequency of the frequency converter, and converting the frequency signal into an actual power signal of the moving sieve through a function; the flow chart is shown in fig. 4.
The system was stable when the feed amount was 28 units. The instability of the actual feeding of the mechanical moving screen jigger is a main factor influencing the gangue discharge effect. By adjusting the feeding amount of the system, the change of the gangue amount (gangue layer) and the gangue discharge frequency output curve in the movable screen is monitored in real time by using the configuration king upper computer software, and then the effects of fuzzy logic control and logic linear control are compared.
Firstly, when the feeding quantity fluctuates within the range of +/-5 KW, a curve chart shown in figure 5 is obtained.
Analysis of the results was performed on the curves of fig. 5: when the feed quantity fluctuates in a small range, the fluctuation amplitudes of the waste rock quantity P1 and P2 in the moving screen in the two controls are the same, and the fluctuation amplitudes of the waste rock discharge frequencies F1 and F2 are also the same, so that the control effect is not different.
② when no material is fed for 40 seconds, obtaining a curve chart as shown in figure 6.
Analysis of the results was performed on the curves of fig. 6: the waste rock discharge frequency F1 has a section of buffer descending frequency and then descends rapidly to reduce the discharge amount of waste rocks; and F2 falls slowly all the time. Meanwhile, the amount of the waste rock P1 in the movable screen is reduced less than that of the waste rock P2, so that the waste rock layer is more stable, and the damage to the existing waste rock layer structure is avoided. When the feed quantity is recovered after 40 seconds, the recovery speed of the fuzzy logic control is higher than that of the logic linear control.
③ when the feed quantity is continuously 50 units in 40 seconds, obtaining a curve chart as shown in figure 7.
Analysis of the results was performed on the curves of fig. 7: the waste rock discharge frequency F1 has a section of buffer ascending frequency, then the frequency rises rapidly, and the discharge capacity of waste rock is increased; and F2 rises slowly all the time. Meanwhile, the rising amount of the gangue amount P1 in the movable screen is less than that of the gangue amount P2, the gangue layer is more stable, and the structure of the gangue layer is prevented from being damaged. In addition, when the feed quantity is recovered after 40 seconds, the recovery speed of the fuzzy logic control is higher than that of the logic linear control.
From the test results of the examples it can be concluded that: the fuzzy logic control system can well solve the problem that a nonlinear, time-varying and hysteresis mathematical model is difficult to establish in the coal washing process of the mechanical moving screen jigging machine; the control effect is good, and stability is strong, can satisfy the user demand.
Claims (3)
1. A fuzzy logic control method of a mechanical movable sieve jig is characterized in that: the method comprises the following steps:
step one, setting a gangue layer power value according to the no-load power of the mechanical movable sieve jig;
step two, acquiring the power P of the main driving motor of the mechanical moving screen jig in real time through a power transmitter, and transmitting the power value of the power P of the main driving motor of the mechanical moving screen jig to a PLC in an analog quantity form;
step three, taking the PLC as a fuzzy controller, and fuzzifying the power deviation e, the deviation change rate delta e and the gangue discharge curve correction rate u of the main driving motor of the mechanical moving screen jig;
fourthly, according to the waste rock discharge control process requirement of the mechanical movable sieve jig, combining practical experience to manufacture a fuzzy rule table, defuzzifying by a weighted average method, and then obtaining a waste rock discharge curve correction rate u;
fifthly, determining a gangue discharge control curve formula Ft according to the rated power of a main driving motor of the mechanical moving screen jigger and the upper and lower limit values set by no-load power;
combining the requirement of the mechanical movable sieve jig on the waste rock discharge wheel motor, and when the thickness of the waste rock layer is lower than the height of a waste rock discharge port, the rotating speed of the waste rock discharge wheel motor is 0; when the thickness of the gangue layer exceeds the height of the gangue outlet, starting a gangue discharge wheel motor; the rotating speed of the motor of the waste rock discharging wheel is continuously increased along with the increase of the thickness of the waste rock layer, and the rotating speed of the waste rock discharging wheel is fastest when the thickness of the waste rock layer reaches the upper edge of the baffle; the formula of the waste rock discharge control curve is as follows:
in the formula: pt is the real-time power of a main driving motor of the mechanical moving screen jig;
ph is an upper limit value of real-time power of a main driving motor of the mechanical moving screen jigger;
p1-lower limit value of real-time power of a main driving motor of the mechanical moving screen jigger;
ft is the real-time frequency of the normal operation of the gangue discharge wheel motor;
sixthly, curvature adjustment is carried out on a waste rock discharge control curve formula by using a waste rock discharge curve correction rate u obtained through fuzzy control, so that the waste rock discharge control curve can carry out fuzzy judgment according to different working conditions, and the frequency value of a waste rock discharge frequency converter is automatically adjusted;
carrying out fuzzy reasoning according to a fuzzy algorithm to obtain a controlled quantity U, and carrying out fuzzy solution on the controlled quantity U by a weighted average method to further obtain a gangue discharge curve correction rate U;
the obtained change rule of the gangue discharge frequency is as follows:
in the formula: a. b is the maximum value and the minimum value of the basic discourse domain of the variables e, delta e and u, and n is the dispersion of the variables e, delta e and u;
according to the fuzzy logic formula:
converting the obtained output quantity into 4-20 mA direct current signals through D/A (digital/analog) to a frequency converter; thereby automatically adjusting the frequency value of the gangue discharge frequency converter.
2. The fuzzy logic control method of the mechanical movable sieve jig according to claim 1, wherein: dividing three signals of the power deviation e, the deviation change rate delta e and the gangue discharge curve correction rate u of the main driving motor of the mechanical moving screen jigger in the third step into 7 fuzzy states, namely: NB, NM, NS, ZO, PS, PM, PB; and the membership function is a triangular function, and then fuzzy reasoning is carried out according to a fuzzy algorithm.
3. The fuzzy logic control method of the mechanical movable sieve jig according to claim 1, wherein: obtaining NxN rules from the fuzzy rule table in the fourth step, wherein each rule corresponds to different power deviation e, deviation change rate delta e and linguistic values of the controlled variables; and then obtaining the fuzzy subsets corresponding to the language values according to the membership function.
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